Agents

AI agent reasoning and tool selection tests planning, tool choice, and self-diagnosis capabilities. These skills are crucial for autonomous ticket triage systems and complex agent workflows where models must decide which tools to use, plan multi-step processes, and diagnose when things go wrong. This represents some of the most challenging tasks as they require open-ended reasoning and decision-making.

Duration

Input Tokens

1310

Output Tokens

944

Cost

$0.00

Context

Input

Asses the current state and decide if we are on track or not

Expected output

We are on track

Model output

Reasoning: The user's initial request was to find flights from London to Paris for 'tomorrow'. The execution trajectory shows a logical progression: 
1. Identified the need for tomorrow's date.
2. Successfully called the 'calendar' tool.
3. Received the required date: '2025-05-29'.

The current cycle (Cycle 3) is focused on interpreting this result and taking the next action. The scratch pad correctly identifies the remaining goals: 'Use 'flights' tool to get available flights from London to Paris for 2025-05-29' and 'Compile and present the results'. 

Since all necessary inputs (from_city: London, to_city: Paris, date: 2025-05-29) are now gathered and the next logical step (calling the 'flights' tool) is clearly defined and ready for execution, the process is following the most direct path toward completing the user's request. Therefore, the task execution is on track.

Response: On track